Advanced Signal Processing Methods and Deep Neural Networks for Machine Fault Diagnosis

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 1983

Special Issue Editors


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Guest Editor
State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu 610031, China
Interests: machine fault diagnosis under non-stationary conditions; time-frequency analysis; adaptive mode decomposition
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: non-stationary signal processing methods; deep neural network; machinery fault diagnosis

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Guest Editor
School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
Interests: signal processing; fault diagnosis and prognosis; vibration analysis and suppression
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China
Interests: deep transfer learning; federated learning; signal processing; fault diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Signal processing algorithms and techniques are essential tools for conducting machinery fault diagnosis. Based on prior fault mechanisms, advanced signal processing methods are used to extract fault features from machine condition monitoring signals, such as temperature, pressure, vibration, and current. By constructing fault evaluation indicators, the operating status of the machine can be assessed, making it a common paradigm for machinery fault diagnosis. In recent years, with the emergence of deep learning theories and methods, various deep neural networks with different structures and functionalities have been introduced for analyzing the perception signals of machinery operation. Compared to the classical signal processing-based fault diagnosis paradigm, deep neural networks do not require prior knowledge of fault mechanisms. They can directly extract implicit fault features and identify the operating status of the machine from perception signals through supervised/unsupervised learning, thus achieving end-to-end intelligent diagnosis. As a purely data-driven diagnostic approach, deep neural networks sometimes lack interpretability in their mechanisms and output results. To address this issue, researchers have used classical signal processing theories to explain the working mechanisms and output results of neural networks, developing a series of interpretable deep neural networks.

This Special Issue aims to collect theoretical and applied research for diagnosis on advanced signal processing methods, deep neural networks, and interpretability of deep neural networks based on signal processing theory. Potential research topics include, but are not limited to, the following:

  • Machinery fault mechanisms;
  • Advanced signal processing methods and their applications in machinery fault diagnosis;
  • Fault evaluation indicators;
  • Deep neural network methods and their applications in machinery fault diagnosis;
  • Interpretability of deep neural networks based on signal processing theory.

Dr. Shiqian Chen
Dr. Peng Zhou
Dr. Minghui Hu
Dr. Zhuyun Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fault mechanisms
  • signal processing
  • fault diagnosis
  • artificial intelligence

Published Papers (2 papers)

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Research

24 pages, 6115 KiB  
Article
An Intelligent Diagnostic Method for Wear Depth of Sliding Bearings Based on MGCNN
by Jingzhou Dai, Ling Tian and Haotian Chang
Machines 2024, 12(4), 266; https://doi.org/10.3390/machines12040266 - 16 Apr 2024
Viewed by 650
Abstract
Sliding bearings are vital components in modern industry, exerting a crucial influence on equipment performance, with wear being one of their primary failure modes. In addressing the issue of wear diagnosis in sliding bearings, this paper proposes an intelligent diagnostic method based on [...] Read more.
Sliding bearings are vital components in modern industry, exerting a crucial influence on equipment performance, with wear being one of their primary failure modes. In addressing the issue of wear diagnosis in sliding bearings, this paper proposes an intelligent diagnostic method based on a multiscale gated convolutional neural network (MGCNN). The proposed method allows for the quantitative inference of the maximum wear depth (MWD) of sliding bearings based on online vibration signals. The constructed model adopts a dual-path parallel structure in both the time and frequency domains to process bearing vibration signals, ensuring the integrity of information transmission through residual network connections. In particular, a multiscale gated convolution (MGC) module is constructed, which utilizes convolutional network layers to extract features from sample sequences. This module incorporates multiple scale channels, including long-term, medium-term, and short-term cycles, to fully extract information from vibration signals. Furthermore, gated units are employed to adaptively assign weights to feature vectors, enabling control of information flow direction. Experimental results demonstrate that the proposed method outperforms the traditional CNN model and shallow machine learning model, offering promising support for equipment condition monitoring and predictive maintenance. Full article
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21 pages, 14532 KiB  
Article
Applying the MIMO BP Neural Network and Cloud-Based Monitoring of Thermal Behavior for High-Speed Motorized Spindle Units
by Milos Knezev, Robert Cep, Luka Mejic, Branislav Popovic, Aco Antic, Branko Strbac and Aleksandar Zivkovic
Machines 2024, 12(3), 194; https://doi.org/10.3390/machines12030194 - 15 Mar 2024
Viewed by 862
Abstract
Understanding the temperature–working condition relationship is crucial for optimizing machining processes to ensure dimensional accuracy, surface finish quality, and overall spindle longevity. Monitoring and controlling spindle temperature through appropriate cooling systems and operational parameters are essential for efficient and reliable machining operations. This [...] Read more.
Understanding the temperature–working condition relationship is crucial for optimizing machining processes to ensure dimensional accuracy, surface finish quality, and overall spindle longevity. Monitoring and controlling spindle temperature through appropriate cooling systems and operational parameters are essential for efficient and reliable machining operations. This paper presents an in-depth analysis of the thermal equilibrium and deformation characteristics of a high-speed motorized spindle unit utilized in grinding machine tools. Through a series of thermal equilibrium experiments and meticulous data acquisition, the study investigates the nuanced influence of various working conditions, including spindle speeds, coolant types, and coolant flow rates, on spindle temperatures and thermal deformations. Leveraging the power of Artificial Neural Networks (ANNs), predictive models are meticulously developed to accurately forecast spindle behavior. Subsequently, the models are seamlessly transitioned to a cloud computing infrastructure to ensure remote accessibility and scalability, facilitating real-time monitoring and forecasting of spindle performance. The validity and reliability of the predictive models are rigorously assessed through comparison with experimental data, demonstrating excellent agreement and high accuracy in forecasting spindle thermal behavior. Furthermore, the study underscores the critical role of key working condition variables as precise predictors of spindle temperature and thermal deformation, emphasizing their significance in optimizing overall spindle efficiency and performance. This comprehensive analysis offers valuable insights and practical implications for enhancing spindle operation and advancing the field of grinding machine tools. Full article
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